mirror of
https://github.com/vladmandic/automatic
synced 2026-09-10 23:08:43 +02:00
Vladimir Mandic
46 lines
2.1 KiB
Python
46 lines
2.1 KiB
Python
import diffusers
|
|
from modules import shared, devices, sd_models
|
|
from modules.logger import log
|
|
|
|
|
|
def load_minimax(checkpoint_info, diffusers_load_config = None, workflow: str | None = None):
|
|
from modules.video_models import video_load
|
|
from modules.modular_load import load_modular_pipe
|
|
repo_id = sd_models.path_to_repo(checkpoint_info)
|
|
sd_models.hf_auth_check(checkpoint_info)
|
|
if repo_id is None or repo_id.lower() == 'none':
|
|
return None
|
|
|
|
offline_args = {'local_files_only': True} if shared.opts.offline_mode else {}
|
|
workflow = (workflow or getattr(checkpoint_info, 'subfolder', None) or 'fl2va').lower() # one repo holds both checkpoint partitions; reference entries select ref2va via the subfolder tag
|
|
log.debug(f'Load model: type=MiniMaxH3 repo="{repo_id}" workflow={workflow} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype}')
|
|
|
|
sd_models.warn_group_offload(min_vram=20)
|
|
repo_cls = diffusers.MiniMaxH3ModularPipeline
|
|
pipe = load_modular_pipe(
|
|
repo_cls,
|
|
repo_id,
|
|
workflow=workflow,
|
|
offline_args=offline_args,
|
|
base=True,
|
|
load_config=diffusers_load_config,
|
|
)
|
|
if pipe is None:
|
|
return None
|
|
|
|
pipe.sd_checkpoint_info = checkpoint_info
|
|
pipe.sdnext_force_offload = True # very large model, so each stage ends with an on-demand offload sweep
|
|
if hasattr(pipe, 'min_duration') and hasattr(pipe, 'fps'):
|
|
pipe.sdnext_supported_min_frames = int(pipe.min_duration * pipe.fps) # fresh pipes report the true floor; still mode gates per instance
|
|
|
|
video_load.loaded_model = None # image-path load invalidates the video tab's name cache
|
|
# if hasattr(pipe, 'vae'):
|
|
# pipe.vae = pipe.vae.to(torch.float16) # minimax loads vae in float32
|
|
if hasattr(pipe, 'vae') and hasattr(pipe.vae, 'enable_tiling'):
|
|
pipe.vae.enable_tiling()
|
|
|
|
from pipelines.minimax.minimax_latents import unpack_latents
|
|
pipe.custom_unpack_latents = unpack_latents # add a helper to unpack the video latents from the block state
|
|
devices.torch_gc()
|
|
return pipe
|